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Automated Trading
This document discusses automated trading and the role of computer science. It describes how traders observe markets to detect patterns and build prediction models. A system is proposed that learns patterns automatically using machine learning. Computer science helps by developing very reactive systems using algorithms to efficiently process high-dimensional market data and make objective trading decisions. Challenges include developing accurate prediction models that address overfitting, non-analytical objective functions, and reducing the dimensionality of order book data to determine a fair price. The document outlines how computer science areas like networks, operating systems, hardware, data management, and design practices help build high-performance automated trading infrastructure.
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Automated Trading
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- 2. What IS Trading/what do traders do Traders observe market behaviour for a long time They detect certain patterns in market data and their mind makes certain prediction models consciously or subconsciously ( intuition ) They try to execute trades as instructed by the abstract model which was learned by their mind Feed historical data to our system Make it automatically learn patterns using ML/Statistics/Pattern Recognition and build prediction models Make it execute trades as instructed by the automated model which was learned by our system How about building a system that learns patterns in the market automatically? But, What is the role of computer science in all this?
- 3. Advantages of automated trading ✖Very reactive Quickly Responds to minor changes in the market ✖ Objective in nature Not subject to emotional bias ✖ Capable of handling very high dimensional data 1000’s of features
- 4. Prediction Problem ✖ GeneralIdea Predict future price under given market conditions/ scenario ✖ Challenge1 - High Dimensional data 1000s of variables/features to characterise given market scenario. Question:Why is that a problem ?? Overfitting?? What if most the the features are correlated ?? How to avoid that ?? Dimensionality Reduction/PCA/subset selection
- 5. Prediction Problem - Continued ✖Challenge2 - Loss function/Objective function How to evaluate if the learned model/pattern is good enough Profit?? Accuracy rate?? Correlation ?? What if an objective function is not analytical. Other Challenges - Overfitting problems (bias / variance tradeoff), Outlier detection What if an objective function is not even continuous
- 6. Limit Order Book BestBid Price Best Ask Price { }Passive Buy Orders Passive Sell Orders Bid Order Count Bid Size Bid Price Ask Price Ask Size Ask Order Count 4 18 3061 3062 50 6 5 40 3060 3063 100 8 10 200 3059 3065 140 13 12 190 3058 3066 110 7 …. …. …. …. …. ….
- 7. Bid Order Count Bid Size Bid Price Ask Price Ask Size Ask Order Count 4 18 30613062 50 6 5 40 3060 3063 100 8 10 200 3059 3065 140 13 12 190 3058 3066 110 7 …. …. …. …. …. …. Place Passive Sell Orders Cancel Passive Sell Orders Aggressively Sell Aggressively Buy 3 7 2 6 1 8 1 4 Ask Queue Place Passive Buy Orders Cancel Passive Buy Orders Bid Queue 7 2 6 3
- 8. Notion of PRice- Reducing Order book Dimensionality✖ No concrete fair price defined ✖ Only order book snapshots and trade history ✖ Total dimensionality of order book is high ✖ How to map this higher dimension data to a single number ( A Notion of fair price ) ??
- 9. Let’s take aneasy problem Total size at Bid Number of orders at Bid Price at highest Bid Price at lowest Ask Number of orders at Ask Total size at Ask 240 25 99 100 40 80 What is your price ? ● Should it be between the bid and the ask ? o How about the mean of the two ? o How should sizes affect it? ● Should the inference be different if the market has been moving a lot and this is just the current snapshot ?
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- 11. ➽Every microsecond matters! ➽Need to be Fast, Optimized and Robust. ➽Several Areas of Computer Science involved. Infrastructure Challenges
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- 14. Infrastructure Pipeline Receiving & Decoding Market Data Processin g & Building Book Strategy Decision Sending Orders Receiving Replies Interpreting marketdata and effectively keeping a track of bid/ask sides
- 15. Infrastructure Pipeline Receiving & Decoding Market Data Processin g & Building Book Strategy Decision Sending Orders Receiving Replies In whichdirection is the market going to move?
- 16. Infrastructure Pipeline Receiving & Decoding Market Data Processin g & Building Book Strategy Decision Sending Orders Receiving Replies Sending neworders or cancelling previously placed orders
- 17. Infrastructure Pipeline Receiving & Decoding Market Data Processin g & Building Book Strategy Decision Sending Orders Receiving Replies What happenedto my order? Did it get placed successfully, or was there any error?
- 18. ➽Receiving exchange datafaster : UDP(Multicast) or TCP ? ➽Colocation servers, direct lines to exchange. ➽Data through Microwave. COMPUTER NETWORKS
- 19. ➽Book building requiresefficient data structures. ➽Using the best algorithms, minimizing latency. ➽Optimizing code for better hardware utilization Data Structures / Algorithms
- 20. ➽Inter Process Communication: Shared Memory or TCP or Multicast ? ➽Multithreaded Applications: Locks, Mutexes, Semaphores ? ➽Context Switches ? Affine process to a single core Messaging Accelerators ( eg. VMA ) : bypass kernel for network input Operating Systems
- 21. ➽Decoding provided datausing Hardware (instead of software)? ➽FPGAs programmed to decode data. ➽Latency benefits due to parallel processing (at Hardware level) Digital Hardware Design
- 22. ➽Managing large amountsof historical exchanges’ data, and running studies on it. ➽Amazon EC2 (compute), S3 (backups) BIG DATA MANAGEMENT
- 23. ➽Large Codebase withcommon code. ➽Design Patterns, Object Oriented, Templated Programming. Design Practices
- 24. Thanks! Any questions? Two Roads TechnologicalSolutions http://www.tworoads.co.in/ careers@tworoads.co.in 08800355641
Editor's Notes
- #7 Will people understand what bid and ask prices are ? How can one sell without buying? Passive orders ?